Publications

Disorganized Communication and Social Dysfunction in Schizophrenia: Emerging Concepts and Methods
Emmanuel Olarewaju
Guillaume Dumas
L. Palaniyappan
2023-09-23
Current Psychiatry Reports (published)
doi.org
Autonomic nervous system modulation during self-induced non-ordinary states of consciousness
Victor Oswald
Audrey Vanhaudenhuyse
Jitka Annen
Charlotte Martial
Aminata Bicego
Floriane Rousseaux
Corine Sombrun
Yann Harel
Marie-Elisabeth Faymonville
Steven Laureys
Karim Jerbi
Olivia Gosseries
2023-09-22
Scientific Reports (published)
doi.org
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
Sébastien Lachapelle
Divyat Mahajan
Ioannis Mitliagkas
Simon Lacoste-Julien
We tackle the problems of latent variables identification and "out-of-support'' image generation in representation learning. We show that bo… (see more)th are possible for a class of decoders that we call additive, which are reminiscent of decoders used for object-centric representation learning (OCRL) and well suited for images that can be decomposed as a sum of object-specific images. We provide conditions under which exactly solving the reconstruction problem using an additive decoder is guaranteed to identify the blocks of latent variables up to permutation and block-wise invertible transformations. This guarantee relies only on very weak assumptions about the distribution of the latent factors, which might present statistical dependencies and have an almost arbitrarily shaped support. Our result provides a new setting where nonlinear independent component analysis (ICA) is possible and adds to our theoretical understanding of OCRL methods. We also show theoretically that additive decoders can generate novel images by recombining observed factors of variations in novel ways, an ability we refer to as Cartesian-product extrapolation. We show empirically that additivity is crucial for both identifiability and extrapolation on simulated data.
openreview.net
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Zhaocheng Zhu
Xinyu Yuan
Mikhail Galkin
Louis-Pascal Xhonneux
Sophie Xhonneux
Ming Zhang
Maxime Gazeau
Jian Tang
openreview.net
Are Diffusion Models Vision-And-Language Reasoners?
Benno Krojer
Elinor Poole-Dayan
Vikram Voleti
Chris Pal
Siva Reddy
Text-conditioned image generation models have recently shown immense qualitative success using denoising diffusion processes. However, unlik… (see more)e discriminative vision-and-language models, it is a non-trivial task to subject these diffusion-based generative models to automatic fine-grained quantitative evaluation of high-level phenomena such as compositionality. Towards this goal, we perform two innovations. First, we transform diffusion-based models (in our case, Stable Diffusion) for any image-text matching (ITM) task using a novel method called DiffusionITM. Second, we introduce the Generative-Discriminative Evaluation Benchmark (GDBench) benchmark with 7 complex vision-and-language tasks, bias evaluation and detailed analysis. We find that Stable Diffusion + DiffusionITM is competitive on many tasks and outperforms CLIP on compositional tasks like like CLEVR and Winoground. We further boost its compositional performance with a transfer setup by fine-tuning on MS-COCO while retaining generative capabilities. We also measure the stereotypical bias in diffusion models, and find that Stable Diffusion 2.1 is, for the most part, less biased than Stable Diffusion 1.5. Overall, our results point in an exciting direction bringing discriminative and generative model evaluation closer. We will release code and benchmark setup soon.
openreview.net
Block-State Transformers
Jonathan Pilault
Mahan Fathi
Orhan Firat
Chris Pal
Pierre-Luc Bacon
Ross Goroshin
2023-09-21
NeurIPS.cc/2023/Conference (poster)
openreview.net
openreview.net
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
Charles Guille-Escuret
Pau Rodriguez
David Vazquez
Ioannis Mitliagkas
Joao Monteiro
openreview.net
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL
Chen Sun
Wannan Yang
Thomas Jiralerspong
Dane Malenfant
Benjamin Alsbury-Nealy
Yoshua Bengio
Blake Richards
In real life, success is often contingent upon multiple critical steps that are distant in time from each other and from the final reward. T… (see more)hese critical steps are challenging to identify with traditional reinforcement learning (RL) methods that rely on the Bellman equation for credit assignment. Here, we present a new RL algorithm that uses offline contrastive learning to hone in on these critical steps. This algorithm, which we call Contrastive Retrospection (ConSpec), can be added to any existing RL algorithm. ConSpec learns a set of prototypes for the critical steps in a task by a novel contrastive loss and delivers an intrinsic reward when the current state matches one of the prototypes. The prototypes in ConSpec provide two key benefits for credit assignment: (i) They enable rapid identification of all the critical steps. (ii) They do so in a readily interpretable manner, enabling out-of-distribution generalization when sensory features are altered. Distinct from other contemporary RL approaches to credit assignment, ConSpec takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon (and ignoring other states) than it is to prospectively predict reward at every taken step. ConSpec greatly improves learning in a diverse set of RL tasks. The code is available at the link: https://github.com/sunchipsster1/ConSpec
openreview.net
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Sharan Vaswani
Amirreza Kazemi
Reza Babanezhad Harikandeh
Nicolas Le Roux
Actor-critic (AC) methods are widely used in reinforcement learning (RL) and benefit from the flexibility of using any policy gradient metho… (see more)d as the actor and value-based method as the critic. The critic is usually trained by minimizing the TD error, an objective that is potentially decorrelated with the true goal of achieving a high reward with the actor. We address this mismatch by designing a joint objective for training the actor and critic in a decision-aware fashion. We use the proposed objective to design a generic, AC algorithm that can easily handle any function approximation. We explicitly characterize the conditions under which the resulting algorithm guarantees monotonic policy improvement, regardless of the choice of the policy and critic parameterization. Instantiating the generic algorithm results in an actor that involves maximizing a sequence of surrogate functions (similar to TRPO, PPO) and a critic that involves minimizing a closely connected objective. Using simple bandit examples, we provably establish the benefit of the proposed critic objective over the standard squared error. Finally, we empirically demonstrate the benefit of our decision-aware actor-critic framework on simple RL problems.
openreview.net
A Definition of Continual Reinforcement Learning
David Abel
Andre Barreto
Benjamin Van Roy
Doina Precup
Hado van Hasselt
Satinder Singh
openreview.net
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
Yang Zhang
Zuobai Zhang
Bozitao Zhong
Sanchit Misra
Jian Tang
Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. A… (see more)ccurately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interactions. Traditional methods are computationally intensive and have limited accuracy, while existing machine learning methods treat the problem as a regression task and overlook the restrictions imposed by the constant covalent bond lengths and angles. In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous perturbation of all four torsional angles, we propose autoregressively generating the four torsional angles from
openreview.net
A Diffusion-Model of Joint Interactive Navigation
Matthew Niedoba
Jonathan Wilder Lavington
Yunpeng Liu
Vasileios Lioutas
Justice Sefas
Xiaoxuan Liang
Dylan Green
Setareh Dabiri
Berend Zwartsenberg
Adam Ścibior
Frank Wood
Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of pr… (see more)erecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios expensive. In this paper, we present DJINN - a diffusion based method of generating traffic scenarios. Our approach jointly diffuses the trajectories of all agents, conditioned on a flexible set of state observations from the past, present, or future. On popular trajectory forecasting datasets, we report state of the art performance on joint trajectory metrics. In addition, we demonstrate how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions including goal-based sampling, behavior-class sampling, and scenario editing.
openreview.net